An important component of knowledge management is organization of documents of all kinds for quick and easy access by all authorized members of a company or organization. A useful and effective way of accomplishing this is to associate one or more categories, chosen from a fixed set of organization-specific knowledge categories, to each document for subsequent use in searching or browsing. Specific categories will vary from organization to organization, but categories may number in the hundreds, thousands, or even tens of thousands for a large, technical company.
A problem is presented, however, regarding how an author or manager of a document can determine all knowledge categories that are appropriate for the document. Although the author may be familiar with knowledge categories used regularly by the author, there are likely many other categories that are appropriate for the document and that would be used by searchers for whom this document would be useful. It would not be feasible for all authors to master the entire set of knowledge categories applicable to the author's documents. Similarly, a person searching for a document likewise needs assistance in determining appropriate categories to search for needed information. For example, an author may presently manually assign controlled search terms, such as thesaurus terms, as key words. Further, a searcher may use a partial string match to identify a list of thesaurus terms as search terms.
Currently, attempts to get authors to assign knowledge categories (that is, content metadata) to documents are not very successful. This is because authors are not inclined to assign such knowledge categories unless such an assignment is easily done. Further, authors cannot be expected to know all categories that are important for the author's documents. If authors were to search for appropriate categories, it is likely that categories found would be limited to categories for which the author was searching but was using inexact terms. Even then, searches using standard string matching would often fail to find the categories sought by the authors.
A hand-built thesaurus of synonyms would be helpful. However, creating such a thesaurus of synonyms would be labor-intensive. Moreover, such a thesaurus would still only help authors find known categories. Menus would allow authors to find additional categories. However, this approach would be limited to very small sets of categories. Someone other than an author searching for a document would be motivated more than an author to construct a search query. Again, those searchers would similarly find it difficult to locate the correct categories.
While there are many aspects to managing corporate knowledge, one key issue is how to organize corporate documents into categories of interest. Traditionally, this step requires a great deal of manual intervention and is very time consuming. There are tools that attempt to automatically process raw text data that potentially contains corporate knowledge. However, these tools generate either linguistically-oriented templates, such as subject, action, and object, or field values for a template-like output, such as a resume that contains an applicant's name, education, address, or the like. While these tools could be useful, they still fall short in organizing documents into categories that are meaningful to a corporate environment and that make the knowledge in them directly and easily accessible to others.
Thus, there is an unmet need in the art for an interactive knowledge management environment that organizes knowledge and that allows the organized knowledge to be searched for documents.